Abstract
Introduction
Inflammation is associated with development of chronic kidney disease (CKD). However, the association of the high-sensitivity C-reactive protein (hs-CRP)/albumin ratio (CAR) on the risk of CKD in the general population is unknown. This study explored the relationship between the CAR and CKD and the ability of this ratio to predict CKD in the general population.
Methods
A total of 47,472 participants in the Kailuan study who met the inclusion criteria in 2010 were selected and grouped using the quartile method. A Cox proportional hazard regression model was used to evaluate the association of the CAR on the risk of CKD. The C-index, net reclassification index (NRI), and overall identification index (IDI) were calculated to evaluate the ability of the CAR to predict CKD.
Results
During a follow-up of 378,383 person-years, CKD events occurred in 6,249 study participants (13.16%). The Cox proportional hazard regression model showed that the hazard ratio (95% confidence interval) for CKD events was 1.18 (1.10–1.28) in the Q3 group and 1.42 (1.32–1.53) in the Q4 group when compared with the Q1 group. Compared with the single index, the C-index, NRI, and IDI values were significantly improved when the CAR was added for prediction of risk of CKD.
Conclusions
A higher CAR was an independent risk factor for CKD. The ability of the CAR to predict CKD was better than that of hs-CRP or albumin. The CAR provides an important reference index for predicting the risk of CKD.
Keywords: Chronic kidney disease, High-sensitivity C-reactive protein/albumin ratio, Risk factors, Predictive value, Inflammation
Introduction
Worldwide, the prevalence of chronic kidney disease (CKD) is between 11 and 13 percent and leads to a high mortality rate [1]. In 2017, approximately 1.2 million deaths worldwide were attributable to CKD, and this number is expected to reach 2.2–4.0 million by 2040 [2]. CKD not only increases the risk of end-stage renal disease but also imposes a heavy economic burden on society and individuals as a result of the medical expenses incurred [3]. At present, it is known that age, gender, obesity, smoking, hypertension, diabetes, and poor lifestyle are the traditional risk factors for CKD [4–7]. However, these traditional risk factors do not fully explain the risk of CKD in the general population.
C-reactive protein (CRP) and CRP-based markers are associated with a variety of inflammatory diseases including COVID-19 infection, chronic hepatitis, coronary heart disease, and thyroiditis [8–11]. However, single markers of inflammation may not fully reveal the outcome of the disease. The high-sensitivity CRP (hs-CRP)/albumin ratio (CAR) is a novel inflammatory index that has been widely used in the study of inflammation-related diseases. An elevated CAR has previously been shown to be positively associated with Crohn’s disease [12], COVID-19 infection [13], acute pancreatitis [14], oral squamous cell carcinoma [15], ovarian malignancies [16], non-small cell lung cancer [17], nasopharyngeal tumors [18], and coronary heart disease [19, 20]. The CAR has also been identified as a prognostic marker of infection, rheumatoid arthritis, and critical illness [21–23] and is strongly linked with kidney disease. Two cross-sectional studies have shown that a high CAR is an independent risk factor for diabetic nephropathy and acute kidney injury [24, 25]. However, cohort studies of CAR on the risk of CKD in the general population are lacking. Therefore, we explored the relationship between CAR and CKD in the general population based on the data from the Kailuan study and explored the significance of CAR in predicting the occurrence of CKD in the general population.
Materials and Methods
Study Design
The Kailuan study is an ongoing prospective cohort study in the Kailuan community of Tangshan, China. Since the project started in 2006, the Kailuan General Hospital and its 11 affiliated hospitals have conducted health check-ups for present staff of the Kailuan Group and its retirees every 2 years. Seven follow-up visits have now been completed. The Kailuan study only measured albumin during the third physical examination in 2010. Therefore, this study only included participants who took part in the third physical examination in 2010.
Individuals who participated in the 2010 annual physical examination had complete hs-CRP and albumin data available and were willing to provide written informed consent to get enrolled. Individuals with a previous history of CKD were excluded, as were those for whom baseline and follow-up estimated glomerular filtration rate (eGFR) and urinary protein data were incomplete and those with a malignant tumor, abnormal liver function, or an acute inflammatory condition. This study was approved by the Ethics Committee of Kailuan General Hospital in accordance with the Declaration of Helsinki. All participants signed informed consent (shown in Fig. 1).
Fig. 1.
Participants included and excluded from this study.
Collection of Demographic and Clinical Data
Trained health care professionals collect information on age, sex, smoking, alcohol consumption, education level, physical exercise, self-reported medical history (e.g., hypertension, diabetes, and cancer), and medication history (e.g., antihypertensive and hypoglycaemic agents) using questionnaires. A trained nurse measures height, weight, and blood pressure. Details of the methods can be found in the literature already published by this research group [26].
Laboratory Testing
On the day of the physical examination, 5 mL of venous blood is drawn from the median cubital vein between 7:00 am and 9:00 am; after the study, participant has fasted for at least 8 h. Biochemical indices are measured using a Hitachi 7600 automatic biochemical analyzer. Serum hs-CRP is determined by immunoturbidimetry. The bromocresol green method is used to measure albumin. In routine urine examination, we use a semi-quantitative method to classify urinary protein content, whereby (−) represents <15 mg/dL, trace represents 15–29 mg/dL, (+) represents 30–300 mg/dL, (++) represents 300–1,000 mg/dL, and (+++) represents >1,000 mg/dL.
Calculation of Related Indicators
In this study, subjects were divided into group Q1, group Q2, group Q3, and group Q4 according to the CAR exposure quartile. The glomerular filtration rate was estimated based on the Chronic Kidney Disease Epidemiology Collaboration equation [27]: eGFR for women: serum creatinine (Scr) ≤62 µmol/L: 144 × (Scr/0.7) to 0.329 × (0.993) age, Scr >62 µmol/L: 144 × (Scr/0.7) to 1.209 × (0.993) age. eGFR for men: Scr ≤80 µmol/L: 141 × (Scr/0.9) to 0.411 × (0.993) age, Scr >80 µmol/L: 141 × (Scr/0.9) to 1.209 × (0.993) age.
Relevant Definitions
CKD was defined as an eGFR <60 mL/min/1.73 m2 and/or positive urine protein [28, 29]. Hypertension was defined as systolic blood pressure ≥140 mm Hg and/or diastolic blood pressure ≥90 mm Hg or as systolic blood pressure <140 mm Hg and diastolic blood pressure <90 mm Hg with a history of diagnosed hypertension or taking blood pressure medication. Abnormal liver function was defined as an alanine aminotransferase >40 U/L and albumin <35 g/L. Acute inflammation was defined as a CRP value >10.00 mg/L [30]. Diabetes mellitus was defined as a fasting blood glucose ≥7.0 mmol/L and/or <7.0 mmol/L with a history of diagnosed diabetes or taking hypoglycaemic drugs. Smoking was defined as smoking an average of at least one cigarette a day in the past year. Alcohol consumption was defined as drinking alcohol at least once a day in the past year. Physical exercise was defined as exercising ≥3 times/week for ≥30 min/time. Higher education was defined as a college degree or above. Body mass index (BMI) was calculated by dividing weight in kilograms by height in meters squared.
Follow-Up and Determination of Outcome Events
The starting point of follow-up was the completion of the third physical examination in 2010, and the follow-up was conducted every 2 years. CKD first occurs as a result event. For subjects with no outcome events or intermediate deaths, the last follow-up time or death occurred at the end of follow-up.
Statistical Analysis
Continuous data that were normally distributed are expressed as the mean ± standard deviation and were compared between groups using analysis of variance. Measurements that did not conform to the normal distribution were represented by the median and were compared between groups using the non-parametric rank-sum (Kruskal-Wallis) test. Categorical data are expressed as the frequency and percentage and were compared between groups using the χ2 test. The Kolmogorov-Smirnov test is used to determine the normality of the data. The missing covariates were filled by multiple interpolations. Subjects were divided into quartile groups according to CAR exposure level, and a Cox proportional hazard regression model was used to analyze the association of the CAR on the risk of CKD. Model 1 was corrected for sex and age. Model 2 was corrected for smoking, alcohol consumption, physical exercise, education level, BMI, triglycerides, low-density and high-density lipoprotein cholesterol, eGFR, hypertension, and diabetes based on model 1. Model 3 was corrected for antihypertensive and hypoglycaemic medication based on model 2. Model 4 was corrected for albumin and hs-CRP in 2010 based on model 3. The Kaplan-Meier method was used to calculate the cumulative incidence of CKD events in the CAR quartile groups, which were compared using the log-rank test. The CKD prediction model constructed by Nelson et al. [31] was used to calculate the C-index, net reclassification index (NRI), and overall identification index (IDI) after adding hs-CRP, albumin, and CAR in 2010. A two-tailed p value of <0.05 was considered statistically significant. The incidence of new-onset CKD was calculated by dividing the number of events by the total person-years of follow-up (/1,000 person-years). The potential nonlinear association between CAR and CKD was analyzed using a restricted cubic spline plot at the fifth, 35th, 65th, and 95th percentile nodes.
In order to explore the association of CAR on CKD in different subgroups, a stratified analysis was conducted according to participant age and sex and used multiplicative interactions for interaction. When the main effects of CAR, age, and sex and their product terms p < 0.05, there is a multiplicative interaction in the Cox regression model. In order to remove the confounding association of antihypertensive and hypoglycaemic agents on the results, a sensitivity analysis was performed after excluding those who took these agents at baseline and during follow-up, respectively, and the above Cox proportional risk regression was repeated. For missing covariates, we use multiple interpolation method to fill them. The missing covariates included HDL-C, LDL-C, TG, BMI, education level, smoking, alcohol consumption, physical activity, and education level. These covariates are missing by <2%. All statistical analyses were performed using SAS version 9.4 (SAS Institute Inc., Cary, NC, USA). p < 0.05 was considered statistically significant. All statistical analyses were performed using SAS version 9.4 (SAS Institute Inc., Cary, NC, USA). A p value of <0.05 was considered statistically significant.
Results
Baseline Data
Of 64,834 individuals who participated in the third physical examination in 2010 and had complete hs-CRP and albumin data available, 3,921 patients with a history of CKD were excluded, 5,826 patients with eGFR and urinary protein data loss were excluded at baseline and during follow-up, and 7,615 patients with malignant tumor, abnormal liver function, and acute inflammation were excluded. A total of 47,472 subjects were included in the final analysis (34,816 were male, accounting for 73.3% of all subjects). The average age was 52.59 ± 12.45 years. Compared with group Q1, the proportions of male, smoking, hypertension, diabetes, antihypertensive drugs, hypoglycemic drugs, and low education level in groups Q2, Q3, and Q4 showed an increasing trend, while the proportions of female, higher education level, drinking, and physical exercise showed a decreasing trend. The levels of age, BMI, systolic blood pressure, diastolic blood pressure, fasting blood glucose, triglyceride, low-density lipoprotein, hypersensitive CRP, and hypersensitive CRP /albumin showed an increasing trend, while the levels of albumin, glomerular filtration rate and high-density lipoprotein showed a decreasing trend. The difference between groups was statistically significant (p < 0.01; shown in Table 1).
Table 1.
Baseline characteristics of all study participants
| Total (N = 47,472) | Quartile 1 (N = 12,258) | Quartile 2 (N = 11,162) | Quartile 3 (N = 12,000) | Quartile 4 (N = 12,052) | p valuea | |
|---|---|---|---|---|---|---|
| Age, years | 52.18±12.36 | 50.23±12.96 | 52.66±12.18 | 52.73±11.96 | 53.19±12.09 | <0.01 |
| Gender | ||||||
| Male, N (%) | 34,816 (73.3) | 8,825 (72.0) | 7,910 (70.9) | 8,927 (74.4) | 9,154 (76.0) | <0.01 |
| Female, N (%) | 12,656 (26.7) | 3,433 (28.0) | 3,252 (29.1) | 3,073 (25.6) | 2,898 (24.0) | <0.01 |
| Education level, N (%) | ||||||
| <University or college | 33,877 (71.4) | 8,037 (65.6) | 7,876 (70.6) | 8,700 (72.5) | 9,264 (76.9) | <0.01 |
| ≥University or college | 13,595 (28.6) | 4,221 (34.4) | 3,286 (29.4) | 3,300 (27.5) | 2,788 (23.1) | <0.01 |
| Current smoker, N (%) | 17,570 (37.0) | 4,324 (35.3) | 4,147 (37.2) | 4,522 (37.7) | 4,577 (38.0) | <0.01 |
| Current drinker, N (%) | 16,204 (34.1) | 4,267 (34.8) | 3,917 (35.1) | 4,087 (34.1) | 3,933 (32.6) | <0.01 |
| Physical activity, N (%) | 6,782 (14.3) | 1,647 (13.4) | 1,991 (17.8) | 1,727 (14.4) | 1,417 (11.8) | <0.01 |
| BMI, kg/m2 | 24.79±3.28 | 23.71±3.09 | 24.48±3.08 | 25.23±3.16 | 25.75±3.41 | <0.01 |
| SBP, mm Hg | 128.94±19.05 | 125.33±18.54 | 128.07±19.05 | 130.45±18.88 | 131.92±19.07 | <0.01 |
| DBP, mm Hg | 83.32±10.70 | 81.47±10.49 | 82.79±10.66 | 84.13±10.61 | 84.89±10.69 | <0.01 |
| ALB, g/L | 46.58±6.38 | 46.92±11.53 | 46.68±2.90 | 46.38±3.01 | 46.35±2.87 | <0.01 |
| FBG, mmol/L | 5.54±1.41 | 5.38±1.18 | 5.45±1.36 | 5.60±1.45 | 5.71±1.59 | <0.01 |
| Tg, mmol/L | 1.24 (0.86–1.86) | 1.09 (0.76–1.62) | 1.24 (0.89–1.78) | 1.31 (0.92–1.99) | 1.36 (0.92–2.02) | <0.01 |
| eGFR, mL/min/1.73 m2 | 93.79±16.98 | 97.47±16.87 | 95.86±15.88 | 92.32±17.00 | 89.59±16.94 | <0.01 |
| LDL-C, mmol/L | 2.51±0.84 | 2.34±0.79 | 2.59±0.78 | 2.58±0.82 | 2.51±0.92 | <0.01 |
| HDL-C, mmol/L | 1.58±0.47 | 1.63±0.50 | 1.69±0.48 | 1.55±0.44 | 1.46±0.41 | <0.01 |
| hs-CRP, mg/L | 1.10 (0.60–2.40) | 0.30 (0.12–0.50) | 0.80 (0.70–0.98) | 1.57 (1.30–1.93) | 3.90 (3.00–5.48) | <0.01 |
| hs-CRP/ALB ratio | 0.02 (0.01–0.05) | 0.01 (0.00–0.01) | 0.02 (0.02–0.02) | 0.03 (0.03–0.04) | 0.08 (0.06–0.12) | <0.01 |
| Antidiabetic treatment, N (%) | 2,512 (5.29) | 521 (4.25) | 612 (5.48) | 680 (5.67) | 699 (5.80) | <0.01 |
| Antihypertension treatment, N (%) | 5,171 (10.9) | 949 (7.74) | 1,161 (10.4) | 1,385 (11.5) | 1,676 (13.9) | <0.01 |
| Diabetes, N (%) | 4,699 (9.90) | 867 (7.07) | 1,030 (9.23) | 1,278 (10.7) | 1,524 (12.6) | <0.01 |
| Hypertension, N (%) | 20,578 (43.3) | 4,294 (35.0) | 4,680 (41.9) | 5,628 (46.9) | 5,976 (49.6) | <0.01 |
ALB, albumin; CAR, high-sensitivity C-reactive protein (hs-CRP)/albumin ratio; DBP, diastolic blood pressure; eGFR, estimated glomerular filtration rate; FBG, fasting blood glucose; HDL-C, high-density lipoprotein cholesterol; hs-CRP, high-sensitivity C-reactive protein; LDL-C, low-density lipoprotein cholesterol; SBP, systolic blood pressure; TG, triglycerides.
aComparison of baseline characteristics between the CAR quartiles.
Incidence of CKD Events in Different CAR Groups
During a mean follow-up of 7.14 ± 2.14 years, 6,249 subjects (13.16%) developed CKD events. The mean age at the time of onset of CKD was 56.84 ± 12.27 years. There were 1,237 cases of CKD in the Q1 group, 1,446 in the Q2 group, 1,626 in the Q3 group, and 1,940 in the Q4 group; the respective incidence rates were 12.59, 15.89, 17.01, and 20.75 per 1,000 person-years (shown in Table 1). The cumulative incidence of CKD increased with increasing CAR (p < 0.0001, log-rank test; shown in Fig. 2).
Fig. 2.
The CAR measured the cumulative incidence of CKD in each group. The abscissa is follow-up time and the ordinate is cumulative incidence. The population was divided into four groups by the quartile method. (Kaplan-Meier curve model was used to calculate the cumulative incidence of CKD events in different CAR level groups, and log-rank test was used for comparison between groups). CAR, high-sensitivity C-reactive protein/albumin ratio.
Cox Proportional Risk Model Analysis of the Likelihood of CKD Events according to CAR Quartile
Cox proportional risk model analysis was performed with CAR quartile as the independent variable, the Q1 group as the control, and the occurrence of CKD as the dependent variable. In model 3, compared with the Q1 group, the hazard ratio (HR, 95% confidence interval [CI]) for CKD was 1.07 (0.989–1.153) in the Q2 group, 1.18 (1.098–1.275) in the Q3 group, and 1.42 (1.318–1.526) in the Q4 group. For every one, standard deviation increase in CAR, the HR (95% CI) was 1.129 (1.104–1.154). As the CAR level increased, there was an increase in the risk of CKD (p for trend, <0.0001). Model 4 was corrected for hs-CRP and albumin in 2010 based on model 3 and similar results were obtained (shown in Table 2). Separate analysis of urinary protein and eGFR was consistent with the above main results (shown in Table 3).
Table 2.
Cox proportional hazard model was used to analyze the risk of CKD in the general population with different CAR levels
| n/N | Incidence rate (per 1,000 person-years) | Model 1 | Model 2 | Model 3 | Model 4 | |||||
|---|---|---|---|---|---|---|---|---|---|---|
| HR (95% CI) | p value | HR (95% CI) | p value | HR (95% CI) | p value | HR (95% CI) | p value | |||
| Quartile 1 | 1,237/12,258 | 12.59 | Ref | Ref | Ref | Ref | ||||
| Quartile 2 | 1,446/11,162 | 15.89 | 1.11 (1.03, 1.20) | 0.01 | 1.07 (0.99, 1.15) | 0.09 | 1.07 (0.99, 1.15) | 0.11 | 1.07 (0.99, 1.15) | 0.25 |
| Quartile 3 | 1,626/12,000 | 17.01 | 1.28 (1.19, 1.38) | <0.001 | 1.17 (1.08, 1.26) | <0.001 | 1.18 (1.10, 1.28) | <0.001 | 1.18 (1.10, 1.27) | <0.001 |
| Quartile 4 | 1,940/12,052 | 20.75 | 1.60 (1.49, 1.71) | <0.001 | 1.39 (1.29, 1.50) | <0.001 | 1.42 (1.32, 1.53) | <0.001 | 1.35 (1.30, 1.47) | <0.001 |
| P for trend | <0.001 | <0.001 | ||||||||
| PerSD | 1.13 (1.10, 1.15) | <0.001 | 1.13 (1.10, 1.15) | <0.001 | ||||||
Model 1: adjusted for age and sex. Model 2: adjusted for age, sex, smoking, alcohol consumption, physical activity, BMI, education level, triglycerides, low-density lipoprotein cholesterol, high-density lipoprotein cholesterol, eGFR, diabetes, and hypertension. Model 3: adjusted for age, sex, smoking, alcohol consumption, physical activity, BMI, education level, triglycerides, low-density lipoprotein cholesterol, high-density lipoprotein cholesterol, eGFR, diabetes, hypertension, hypoglycaemic agents, and antihypertensive agents. Model 4: adjusted for all the variables in model 3 and albumin and hsCRP (2010). CAR, high-sensitivity C-reactive protein/albumin ratio; CI, confidence interval; HR, hazard ratio; SD, standard deviation.
Table 3.
Cox proportional hazard model was used to analyze the risk of CKD in the general population with different CAR levels (urine protein and eGFR)
| Model 1 | Model 2 | Model 3 | Model 4 | |||||
|---|---|---|---|---|---|---|---|---|
| HR (95% CI) | p value | HR (95% CI) | p value | HR (95% CI) | p value | HR (95% CI) | p value | |
| U_Pro | ||||||||
| Quartile 1 | Ref | Ref | Ref | Ref | ||||
| Quartile 2 | 0.98 (0.89, 1.08) | 0.73 | 0.94 (0.85, 1.04) | 0.21 | 0.94 (0.85, 1.03) | 0.19 | 0.94 (0.85, 1.03) | 0.19 |
| Quartile 3 | 1.25 (1.14, 1.37) | <0.001 | 1.10 (1.05, 1.20) | 0.04 | 1.11 (1.10, 1.22) | 0.03 | 1.11 (1.10, 1.22) | 0.03 |
| Quartile 4 | 1.45 (1.33, 1.59) | <0.001 | 1.20 (1.10, 1.32) | <0.001 | 1.22 (1.11, 1.34) | <0.001 | 1.19 (1.08, 1.32) | 0.0006 |
| P for trend | <0.001 | <0.001 | ||||||
| PerSD | 1.08 (1.05, 1.11) | <0.001 | 1.08 (1.05, 1.12) | <0.001 | ||||
| eGFR | ||||||||
| Quartile 1 | Ref | Ref | Ref | Ref | ||||
| Quartile 2 | 1.28 (1.43, 1.43) | <0.001 | 1.26 (1.12, 1.40) | <0.001 | 1.25 (1.12, 1.40) | <0.001 | 1.25 (1.12, 1.40) | <0.001 |
| Quartile 3 | 1.36 (1.22, 1.52) | <0.001 | 1.28 (1.15, 1.43) | <0.001 | 1.31 (1.17, 1.46) | <0.001 | 1.29 (1.15, 1.44) | <0.001 |
| Quartile 4 | 1.87 (1.68, 2.07) | <0.001 | 1.68 (1.52, 1.87) | <0.001 | 1.74 (1.57, 1.93) | <0.001 | 1.62 (1.45, 1.82) | <0.001 |
| P for trend | <0.001 | <0.001 | ||||||
| PerSD | 1.13 (1.10, 1.15) | <0.001 | 1.13 (1.10, 1.15) | <0.001 | ||||
Model 1: adjusted for age and sex. Model 2: adjusted for age, sex, smoking, alcohol consumption, physical activity, BMI, education level, triglycerides, low-density lipoprotein cholesterol, high-density lipoprotein cholesterol, eGFR, diabetes, and hypertension. Model 3: adjusted for age, sex, smoking, alcohol consumption, physical activity, BMI, education level, triglycerides, low-density lipoprotein cholesterol, high-density lipoprotein cholesterol, eGFR, diabetes, hypertension, hypoglycaemic agents, and antihypertensive agents. Model 4: adjusted for all the variables in model 3 and albumin and hsCRP (2010). CAR, high-sensitivity C-reactive protein/albumin ratio; CI, confidence interval; HR, hazard ratio; SD, standard deviation.
We found that the CAR interacted with age and sex. In the Q4 group, the HR (95% CI) was 1.379 (1.257–1.513) in the group aged ≤60 years and 1.440 (1.278–1.624) in the group aged >60 years. Similar results were obtained when stratified by sex (shown in Table 4). The restricted cubic spline results showed that after adjusting for confounding factors, the p value was 0.01 for the linear correlation and 0.06 for the nonlinear correlation between CAR and CKD, indicating a linear relationship between the CAR and CKD (shown in Fig. 3).
Table 4.
Stratification analysis
| Quartile 1 | Quartile 2, HR (95% CI) | Quartile 3, HR (95% CI) | Quartile 4, HR (95% CI) | p interaction | ||
|---|---|---|---|---|---|---|
| Age | <0.001 | |||||
| ≤60 | Model 3 | Ref | 1.05 (0.95, 1.16) | 1.17 (1.06, 1.28) | 1.38 (1.26, 1.51) | |
| >60 | Model 3 | Ref | 1.11 (0.98, 1.25) | 1.19 (1.05, 1.34) | 1.44 (1.28, 1.62) | |
| Gender | 0.002 | |||||
| Male | Model 3 | Ref | 1.06 (0.97, 1.16) | 1.18 (1.08, 1.28) | 1.49 (1.28, 1.61) | |
| Female | Model 3 | Ref | 1.07 (0.93, 1.24) | 1.14 (0.98, 1.31) | 1.43 (1.23, 1.55) |
Stratification by age: model 3: adjusted for sex, smoking, alcohol consumption, physical activity, BMI, education level, triglycerides, low-density lipoprotein cholesterol, high-density lipoprotein cholesterol, eGFR, diabetes, hypertension, hypoglycaemic agents, and antihypertensive agents. Stratification by sex: model 3: adjusted for age, smoking, alcohol consumption, physical activity, BMI, education level, triglycerides, low-density lipoprotein cholesterol, high-density lipoprotein cholesterol, eGFR, diabetes, hypertension, hypoglycaemic agents, and antihypertensive agents. CAR, high-sensitivity C-reactive protein/albumin ratio.
Fig. 3.
The potential nonlinear association between CAR and risk of CKD. The X-axis is the CAR, and the Y-axis is the risk ratio with CI. The linear relation p value is p < 0.01, and the nonlinear relation p value is p = 0.1. (Nodes were intercepted at the 5th, 35th, 65th, and 95th percents of CAR, and adjusted variables were as follows: adjusted for age, sex, smoking, alcohol consumption, physical activity, BMI, Edu, TG, LDL-C, HDL-C, eGFR, diabetes, hypertension). CAR, high-sensitivity C-reactive protein/albumin ratio.
Sensitivity Analysis
The group taking antihypertensive medication (n = 5,162) at baseline and during follow-up (n = 7,718) and the group taking hypoglycaemic medication at baseline (n = 2,512) and during follow-up (n = 1,392) were, respectively, removed, and the Cox proportional risk regression model was repeated. The results of the sensitivity analysis were basically consistent with those of the previous major studies, indicating no significant change in the association between the CAR and CKD and that the results are robust (shown in Table 5).
Table 5.
Cox proportional hazard model was used to analyze the risk of CKD in the general population with different CAR levels (sensitivity analysis: blood pressure and hypoglycemic drug populations were excluded)
| n/N | Incidence rate (per 1,000 person-years) | Model 1 | Model 2 | Model 3 | |||||
|---|---|---|---|---|---|---|---|---|---|
| HR (95% CI) | p value | HR (95% CI) | p value | HR (95% CI) | p value | ||||
| Quartile 1 | 757/9,313 | 10.06 | Ref | Ref | Ref | ||||
| Quartile 2 | 786/7,798 | 12.20 | 1.07 (0.97, 1.19) | 0.17 | 1.05 (0.95, 1.16) | 0.40 | 1.05 (0.95, 1.16) | 0.33 | |
| Quartile 3 | 863/7,981 | 13.40 | 1.27 (1.150, 1.40) | <0.001 | 1.20 (1.08, 1.32) | <0.001 | 1.21 (1.09, 1.33) | <0.001 | |
| Quartile 4 | 984/7,518 | 16.45 | 1.59 (1.44, 1.74) | <0.001 | 1.46 (1.33, 1.61) | <0.001 | 1.40 (1.26, 1.56) | <0.001 | |
| P for trend | <0.001 | ||||||||
| PerSD | 1.16 (1.12, 1.19) | <0.001 | |||||||
Model 1: adjusted for age and sex.
Model 2: adjusted for age, sex, smoking, alcohol consumption, physical activity, BMI, education level, triglycerides, low-density lipoprotein cholesterol, high-density lipoprotein cholesterol, eGFR, diabetes, and hypertension.
Model 3: adjusted for all the variables in model 2 and albumin and hsCRP (2010). CAR, high-sensitivity C-reactive protein/albumin ratio; CI, confidence interval; HR, hazard ratio; SD, standard deviation.
C-Index, NRI, and IDI
Using the CKD prediction model established by Nelson et al. [31], which included age, sex, BMI, eGFR, smoking, urinary protein, hypertension, and cardiovascular history as the basic variables and adding hs-CRP, albumin, and the CAR in 2010, the predictive ability of the model was improved. Compared with baseline hs-CRP and albumin, the C-index, NRI, and IDI of adding CAR significantly improved the ability to predict the risk of CKD (shown in Table 6).
Table 6.
C-index, IDI, and NRI of the base model with and without CAR, CRP, or albumin for CKD
| C-index | IDI | p for IDI | NRI | p for NRI | |
|---|---|---|---|---|---|
| CKD | |||||
| Base model | 0.6253 | Ref | Ref | Ref | Ref |
| Base + CRP | 0.6329 | 0.0004 | 0.0005 | 0.0956 | <0.0001 |
| Base + ALB | 0.6250 | 0.0002 | 0.0015 | 0.0117 | 0.86 |
| Base + CAR | 0.6674 | 0.0009 | <0.0001 | 0.1186 | <0.0001 |
Base model includes age, sex, eGFR, cardiovascular history, smoking, hypertension, BMI, and proteinuria. IDI, overall identification index; NRI, net reclassification index; hs-CRP, high-sensitivity C-reactive protein; ALB, albumin; CAR, high-sensitivity C-reactive protein (hs-CRP)/albumin ratio.
Discussion
Our important finding is that CAR, a marker of inflammation that is a risk factor for CKD in the general population, is associated with the risk of new CKD in a potential nonlinear association independent of other risk factors. The ability of CAR to predict CKD was better than that of hs-CRP and albumin.
During 378,383 person-years of follow-up, we found that the CAR was positively associated with the risk of CKD in the general population, with an increased risk of 18% in the Q3 group and of 42% in the Q4 group when compared with the Q1 group. The risk of CKD increased by 13% for each additional standard deviation in the CAR. Although there have been no previous cohort studies of the impact of the CAR on the risk of developing CKD in the general population, two cross-sectional studies have shown that this ratio is strongly associated with the risk of developing CKD in high-risk populations. Bilgin et al. [24] found that a higher CAR was an independent risk factor for diabetic nephropathy (OR: 3.479, 95% CI: 2.24–5.45). Simsek et al. [25] also found that a high CAR was an independent risk factor for acute kidney injury in patients with non-ST-segment elevation myocardial infarction (OR: 1.36, 95% CI: 1.17–1.57). Our findings extend our correlation of the association between the CAR and CKD.
Previous studies have shown that age and sex are risk factors for the occurrence and development of albuminuria and CKD [32–34]. Therefore, we stratified our data by age and sex. In the group aged ≤60 years, the risk of CKD increased by 17% in the Q3 group and by 38% in the Q4 group. In the group aged >60 years, the risk of developing CKD increased by 19% in the Q3 group and by 44% in the Q4 group. Bilgin et al. [24] also found that the association between a high CAR and the risk of CKD in people with type 2 diabetes was more significant in those aged >60 years. Furthermore, in the Q4 group, there was a 43% increased risk of developing CKD in women and a 49% increased risk in men. A study that included 1,470 patients with type 2 diabetes found that male sex was an independent risk factor for decreased eGFR in type 2 diabetes [35]. Our results suggest that the association of the CAR on the risk of CKD is age-dependent and sex-dependent. More prospective cohort studies on age and sex dependence are needed to confirm our present results, considering that most of the individuals in our cohort were middle-aged and elderly and the female population was small.
Previous studies have found that blood pressure and sugar drugs can protect the kidneys. To rule out confounding association of blood pressure and hypoglycemic agents on CAR and CKD at baseline and follow-up, we performed a sensitivity analysis that showed a 4% increase in the risk of CKD compared to the previous primary outcome. These results suggest that taking antihypertensive drugs and hypoglycemic drugs can reduce the risk of developing CKD in people with high CAR levels, which is similar to previous findings [36, 37]. The prediction model of CKD established by Nelson et al. [31] in 2010 was used to calculate C-index, NRI, and IDI after adding hs-CRP, albumin, and CAR. Compared with baseline hs-CRP and albumin, CAR C-index, NRI, and IDI significantly improved the prediction of CKD risk. The results showed that CAR was superior to hs-CRP and albumin in predicting the risk of developing CKD. CAR is an important clinical indicator for predicting the risk of developing CKD in the general population.
The mechanism via which inflammation is involved in the pathogenesis of CKD is not well understood. Mihai et al. [38] believed that inflammation is not only the result but also the cause of CKD and runs through the occurrence and progression of CKD. hs-CRP is not only a marker of inflammation but also participates in the inflammatory response [39]. High circulating hs-CRP levels can bind to damaged renal tissue cells and deposit in renal tissues, including the glomeruli, tubule interstitium, and peritubular microcirculation [40, 41], promoting development of an inflammatory response in renal tissues. An increased hs-CRP level can also promote enhancement of oxidative stress, which plays an important role in development of nephropathy and its complications, causing peroxidation of phospholipids in the basement membrane of glomerular capillaries, resulting in increased permeability of the basement membrane of glomeruli [42] and ultimately kidney disease. Furthermore, albumin can reflect nutritional status and is negatively correlated with inflammation. Patients with CKD have an increased sensitivity to oxidative stress, partly because of lower levels of antioxidants [43]. Serum albumin is considered to be the most important antioxidant in the blood [44, 45]. The increase in CAR can be explained by the increased level of inflammation in the body and the decreased anti-inflammatory association, the combined association of which promotes development of CKD.
The results of this study have significance in terms of clinical guidance. First, its findings confirm that a higher CAR is a risk factor for CKD in the general population regardless of whether traditional risk factors are present. Therefore, CAR can be considered a novel clinical predictor that can be used to prevent CKD in the general population. Second, CAR is a cost-effective and readily available indicator, which is more accessible than other inflammatory markers. As a compound indicator, CAR can sensitively reflect the inflammatory state of the body and may better reveal the outcome of the disease than a single inflammatory indicator. Finally, for individuals with a higher CAR, the risk of CKD can be reduced by reducing the CAR to an ideal level.
This study has some limitations. First, urine protein tests are semi-quantitative and based on single urine samples rather than a quantitative method using 24-h urine samples, which may lead to misclassification of CKD. However, compared with quantitative methods [46], semi-quantitative methods have been widely used in large-scale epidemiological studies owing to their higher sensitivity (93.3%) and specificity (91.6%), simplicity, rapid use, and cost-effectiveness. Second, there were residual confounding factors that required adjustment, such as diseases and medications that increase hs-CRP and decrease albumin. Third, we only analyzed the relationship between the CAR and CKD at baseline, so other measures (such as trajectory and cumulative exposure) are needed to assess the risk of developing CKD with a dynamic CAR.
Conclusion
This Kailuan-based study found that a higher CAR is a risk factor for CKD and that there is a linear relationship between CAR and CKD. The ability of CAR to predict CKD was better than that of hs-CRP or albumin. The CAR provides an important reference index for predicting the risk of CKD.
Acknowledgments
The authors thank the participants of the Kailuan study and the laboratory staff in the Department of Cardiology at Kailuan Hospital for their contributions to this research. We thank LiwenBianji (Edanz) (www.liwenbianji.cn) for editing the English text of a draft of this manuscript.
Statement of Ethics
This study was approved by the Ethics Committee of Kailuan General Hospital in accordance with the Declaration of Helsinki. The approval number is ChiCTR-TNRC-11001489. All participants signed informed consent.
Conflict of Interest Statement
The authors declare that there is no conflict of interest regarding publication of this paper.
Funding Sources
This work was supported by a grant from the National Natural Science Foundation of China (Grant No. 81970359). Supported by the project of High level group for research and innovation of School of Public Health, North China University of Science and Technology (KYTD202311).
Author Contributions
Zhang Zihao conceived the study, analyzed the data, and wrote the first draft. Liu Peipei, Yang Ling, Naihui Zhao, WenLi Ou, XiaoFu Zhang, Shuohua Chen, Yinggen Zhang, ShouLing Wu, and Xiuhong Yang contributed to the analysis and interpretation of the data. All authors reviewed and edited the manuscript and approved its submission.
Funding Statement
This work was supported by a grant from the National Natural Science Foundation of China (Grant No. 81970359). Supported by the project of High level group for research and innovation of School of Public Health, North China University of Science and Technology (KYTD202311).
Data Availability Statement
Data are available with the permission of the Department of Cardiology, Kailuan General Hospital. The data supporting the results of this study are not publicly available because they contain information that could compromise the privacy of study participants but are available from the corresponding author upon reasonable request.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
Data are available with the permission of the Department of Cardiology, Kailuan General Hospital. The data supporting the results of this study are not publicly available because they contain information that could compromise the privacy of study participants but are available from the corresponding author upon reasonable request.
